Download Spatial models reveal the microclimatic buffering capacity of old

RESEARCH ARTICLE
ECOLOGY
Spatial models reveal the microclimatic buffering
capacity of old-growth forests
2016 © The Authors, some rights reserved;
exclusive licensee American Association for
the Advancement of Science. Distributed
under a Creative Commons Attribution
NonCommercial License 4.0 (CC BY-NC).
10.1126/sciadv.1501392
Sarah J. K. Frey,1* Adam S. Hadley,1 Sherri L. Johnson,2 Mark Schulze,1 Julia A. Jones,3 Matthew G. Betts1*
INTRODUCTION
Macroscale climate patterns are well known to influence range-wide
suitability for biota. However, local-scale climate (hereafter microclimate) is often most relevant to animal behavior and demography
(1). Reconciling this mismatch between global climate models and
the scale at which organisms experience their environment should
therefore improve our understanding of biodiversity responses to climate change (2, 3). Furthermore, in heterogeneous mountain landscapes
with complex thermal regimes (4), climate-sensitive species have the
potential to disperse to, and persist in, favorable microclimatic conditions (5). Coarse-scale climate data are not as accurate for predicting
trends in mountains, influencing our ability to assess climate impacts
(6). Identification of factors that generate particular microclimates will
help focus conservation efforts to lessen the impacts of climate change
on biodiversity (7), which are expected to be particularly substantial in
mountainous regions (8). However, to date, the coarse resolution of most
land cover and climate data has precluded such analysis (9).
The decoupling of the surface temperature conditions from those of
the troposphere is commonly attributed to two main factors in mountainous areas: (i) local air-flow dynamics, such as cold air drainage and
pooling, and (ii) variations in slope and aspect (microtopography) (7).
However, vegetation also has the potential to influence microclimatic
patterns via its effects on solar radiation, wind exposure, interception
of precipitation, and retention of understory humidity. Indeed, the influence of vegetation on microclimate has long been recognized (10, 11)
and is the reason why long-term weather stations are situated in open
areas. Unfortunately, such sampling strategies have precluded infer-
1
Forest Biodiversity Research Network, Department of Forest Ecosystems and Society,
Oregon State University, Corvallis, OR 97331, USA. 2U.S. Forest Service, Pacific Northwest
Research Station, Corvallis, OR 97331, USA. 3Geography, College of Earth, Ocean, and
Atmospheric Sciences (CEOAS), Oregon State University, Corvallis, OR 97331, USA.
*Corresponding author. E-mail: [email protected], [email protected] (S.J.K.F.);
[email protected] (M.G.B.)
Frey et al. Sci. Adv. 2016; 2 : e1501392
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ences about the relative influences of microtopography and vegetation
structure on mediating microclimate (2).
If particular vegetation structural characteristics can abate the
effects of regional climate change (12), land management has the
potential to either amplify or buffer these effects on biodiversity
(13). Given the rapid global changes in land use (14), it is critical to
understand the degree to which management influences microclimate. An increasing proportion of the world’s forests are secondary,
transformed forests (14, 15). Therefore, it is essential to understand the
implications of secondary forests for climate and biodiversity. Here, we
examine whether the structural characteristics present in old-growth
forests (for example, heterogeneous canopies, high biomass, and complex vertical structure) increase site-scale thermal buffering capacity
over more structurally simple, but mature plantation forest stands.
The substantial biomass associated with western old-growth forests
might be expected to result in slower rates of warming during summer
months (16). Alternatively, the closed and homogeneous canopy conditions of old (>50-year-old) forest plantations could prevent rapid sitelevel warming through reduced solar radiation, thereby moderating
climate (17). Although previous work has examined the effects of
substantial differences in canopy cover on microclimate (4, 18–22), to
our knowledge, ours is the first broad-scale test of whether subtle
changes in forest structure due to differing management practices influence forest temperature regimes (Fig. 1). Given that old-growth forests
continue to decline globally (23) and that plantations continue to proliferate (24), understanding microclimatic impacts is of great conservation importance.
In 2012 and 2013, we collected understory air temperatures at high
spatial resolution across a complex mountainous landscape at the H. J.
Andrews Experimental Forest (HJA) in Oregon, USA. We obtained
fine-resolution (5 m) data on topography and vegetation structure using
LiDAR (light detection and ranging). We then used machine-learning
techniques [that is, boosted regression trees (BRTs)] to map predicted
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Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from
coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is
thought to be influenced by combined effects of elevation, microtopography, and vegetation, but their relative
effects at fine spatial scales are poorly known. We used boosted regression trees to model the spatial distribution
of fine-scale, under-canopy air temperatures in mountainous terrain. Spatial models predicted observed independent test data well (r = 0.87). As expected, elevation strongly predicted temperatures, but vegetation and
microtopography also exerted critical effects. Old-growth vegetation characteristics, measured using LiDAR (light
detection and ranging), appeared to have an insulating effect; maximum spring monthly temperatures decreased
by 2.5°C across the observed gradient in old-growth structure. These cooling effects across a gradient in forest
structure are of similar magnitude to 50-year forecasts of the Intergovernmental Panel on Climate Change and
therefore have the potential to mitigate climate warming at local scales. Management strategies to conserve
old-growth characteristics and to curb current rates of primary forest loss could maintain microrefugia, enhancing
biodiversity persistence in mountainous systems under climate warming.
RESEARCH ARTICLE
thermal properties at the landscape scale and to test the hypothesis that
vegetation structure mediates under-canopy microclimates.
RESULTS
All models performed well when tested on independent data (table S1).
The cross-validation correlations were high, showing substantial congruence between training and test data [mean ± SD (range): 2012, r =
0.87 ± 0.09 (0.69 to 0.98); 2013, r = 0.84 ± 0.11 (0.64 to 0.96)]. This
performance was not caused by spatial dependency in the data (table S1).
These results indicate that BRTs, which are now used extensively in
species distribution models (SDMs) (25), seem to offer a powerful new
approach to examining spatial distributions in abiotic conditions. As in
SDM applications, the advantage of such machine-learning methods
lies in their capacity to incorporate many independent variables and
their flexibility to include nonlinearities and variable interactions. Although parametric alternatives are available (for example, generalized
linear models), our results indicate that BRTs also represent a promising
option for distinguishing the relative importance (RI) of complex climate drivers and for generating detailed spatial climate predictions
(fig. S1).
Elevation was the dominant predictor for most of the temperature
metrics (Fig. 2), including cumulative degree days (CDDs); monthly minimum, maximum, and mean temperatures during spring-summer (April
to June); and minimum temperature of the coldest month (Fig. 3A).
High-elevation sites were generally cooler and had fewer CDDs. Microtopographic features showed high RI for predicting CDDs during winterspring (January to March; RI 2012: 45.9%; RI 2013: 53.9%) and maximum temperature of the warmest month (RI 2012: 37.9%; RI 2013:
32.2%; Fig. 3C). Steeper, exposed, south-facing sites were generally
warmer and more variable, and accumulated more CDDs (fig. S2).
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Among all temperature metrics, maximum temperature of the
warmest month (RI 2012: 35.5%; RI 2013: 39.8%) and variability in
weekly spring-summer temperature (RI 2012: 36.7%; RI 2013: 10.2%)
were most strongly influenced by vegetation structure (that is, canopy
height, biomass, understory cover, and vertical structure; Fig. 2). Vegetation structure also had a strong effect on mean monthly maximum
temperature from April to June (RI 2012: 20.4%; RI 2013: 18.9%; Fig. 2).
Furthermore, vegetation structure was an important predictor for temperature variability (RI 2012: 28.9%; RI 2013: 31.6%) and CDDs during
winter (RI 2012: 31.2%; RI 2013: 34.3%). Sites with old-growth forest
traits (for example, taller canopies, higher biomass, and more complex
vertical structure) had reduced temperatures and greater temperature
stability (fig. S2). Old-forest characteristics, such as taller canopies, more
canopy cover >10 m, and biomass >500 Mg/ha, reduced maximum
temperature of the warmest month and mean monthly maximum temperature from April to June (fig. S3, A to C). Old-forest traits also had an
important influence on climate variability; for example, increased coefficient of variation in canopy height and greater midcanopy cover
(2 to 10 m) both reduced variability in mean weekly temperature from
January to March (fig. S3, D and E). Areas with the lowest biomass
variability (for example, even-aged stands such as plantations) showed
more microclimate variability in this winter-spring transition (fig. S3F).
However, topographic position (valleys versus topographically exposed sites) appeared to accentuate the effect of forest structure; exposed
sites with low variability in biomass (for example, plantation stands)
accumulated the most degree days (fig. S4A). A vegetation-elevation interaction revealed that maximum monthly temperature during springsummer was lowest at high elevations with high amounts of canopy cover
surrounding a site (fig. S4C). In both years and for most temperature
metrics, variables at the local scale (25-m radius) had a greater relative
influence than variables averaged across a 250-m radius [overall average RI at the local scale (25 m) across all metrics: 2012: 60.7 ± 10%;
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Fig. 1. The high biomass, tall canopies, and vertical structure of old-growth forests are associated with lower spring maximum temperatures
than in mature plantations. Photos of old-growth (A) and mature plantation (B) forest stands at the H. J. Andrews Experimental Forest (HJA) in Oregon,
USA. [photo credit: Matthew Betts, Oregon State University].
RESEARCH ARTICLE
Fig. 3. Spatially predicted maps of minimum temperature of the coldest month and maximum temperature of the warmest month (in degrees
Celsius) based on BRT models. Minimum temperatures (A) were primarily influenced by elevation (B), but maximum temperatures (C) were primarily a
function of vegetation and microtopography (D). Maps of the elevational gradient (B; in meters) and canopy height (D; in meters) based on LiDAR from 2008 at
the HJA. Black dots show the 183 temperature sampling locations. The location of the HJA in the western United States is shown in (A).
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Fig. 2. Relative influence (RI) of variables describing elevation (ELV), microtopography (TOPO), and vegetation structure (VEG) for each
temperature metric. RI values for 2012 (A) and 2013 (B) were derived from the number of times each variable was selected in the process of model
building using boosted regression trees (BRTs). Overall, elevation had the strongest influence on air temperature patterns in the HJA, but microtopography and vegetation also exerted important effects, particularly for maximum temperature of the warmest month, variability measures, and
cumulative degree days (CDD) in the winter-spring transition.
RESEARCH ARTICLE
2013: 60.4 ± 10.1%]. However, vegetation metrics tended to have more
influence at the broader (250-m) spatial scale (fig. S5). Despite interannual differences in the RI of variables, we found remarkable betweenyear consistency in both predicted and observed under-canopy thermal
conditions across sites (7 of 10 variables with r > 0.9; table S2 and figs. S6
and S7). This interannual consistency in site-level conditions, which
occurred despite substantial differences in annual climatic conditions,
lends support to the notion that thermally buffered sites may provide
temporally consistent refugia for biodiversity (7).
Despite being well suited to prediction given our complexity of input
variables, BRTs do not provide information on effect sizes (that is,
regression coefficients) (25). Therefore, we used principal components
analysis (PCA) to integrate vegetation structure variables into a reduced
number of components (table S3). The first two principal components
of the PCA explained 74.7% of the variability [principal component 1
(PC1) = 44.7%; principal component 2 (PC2) = 30%]. The first component (PC1) strongly reflected a gradient in forest structure from closedcanopy plantations to mature/old-growth forests (Figs. 1 and 4A); this
gradient represents the predominant forest types on federal land in the
region (26). Sites with low PC1 values had less biomass (mean and SD),
lower canopies (mean and SD), and less cover (2 to 10 and >10 m) (table
S3). The individual LiDAR metrics effectively distinguished between
plantation sites and mature/old-growth forest sites (table S4); a discriminant function analysis (27) showed that prediction accuracy was
85.3% for plantation sites and 90.4% for mature/old-growth forest
sites. Furthermore, our LiDAR metrics were congruent with previously
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Fig. 4. Differences in microclimate conditions across a gradient in forest structure. (A) Principal components analysis (PCA) showing how vegetation structure metrics differ between mature/old-growth forest sites and plantations. The ellipses represent 68% of the data assuming a normal
distribution in each category (plantation and mature/old growth). (B) Three-dimensional LiDAR-generated images of plantation forests [(i) side view; (ii)
overhead view] and old-growth forests [(iii) side view; (iv) overhead view] at the Andrews Forest. (C and D) Results from generalized linear mixed
models show the modeled relationship between forest structure [PC1, the first component of a PCA on forest structure variables (A)] and the residuals
from an elevation-only model of mean monthly maximum during April to June (C) and mean monthly minimum during April to June (D) after
accounting for the effects of elevation. Closed circles represent 2012 and open circles represent 2013. Maximum monthly temperatures (C) decreased
by 2.5°C (95% confidence interval, 1.7° to 3.2°C) and observed minimum temperatures (D) increased by 0.7°C (0.3° to 1.1°C) across the observed structure gradient from plantation to old-growth forest.
Frey et al. Sci. Adv. 2016; 2 : e1501392
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RESEARCH ARTICLE
Table 1. Generalized linear mixed model results for the relationship between temperature metrics and the first component of a PCA (PC1)
representing a gradient in vegetation structure. Data from 2012 and 2013 were combined and “site” was included as a random effect in all models.
Lower PC1 values indicate forest plantations and higher values indicate old-growth forests. “Change in temperature metrics” reports the average
difference in temperature (°C) or degree days (dd) across the range of PC1 values. The effect of old-growth structure (PC1) on microclimate was
consistent between years for most variables (“No year effects”). Effects of old-growth forests were stronger in 2012, and the direction of old-growth
effects remained consistent for all but SD in weekly temperature. We included elevation (ELV) in all models to statistically account for elevation differences. Elevation had a significant effect on all models (P < 0.0001), except for SD in weekly temperature from January to March (P = 0.062). Coefficients
from the interaction models include the 2012 intercept (^b 0 2012), the slope of the 2012 PC1 effect (^b 1 PC1 2012), the 2013 intercept presented as the
difference from the 2012 intercept (D^b 0 2013), and the 2013 PC1 effect presented as the difference from the 2012 PC1 effect (PC1 D^b 1 2013). P values in
boldface indicate a statistically significant effect of PC1 on temperature metrics at P < 0.05. LCL, lower 95% confidence limit; UCL, upper 95% confidence limit.
Intercept
Variable
^b 0
PC1
SE
^b 1
SE
Change in temperature metrics ~ PC1
P
Units
Change
LCL
UCL
CDD > 0°C January to March
178.66
3.66
−0.47
3.92
0.9051
dd
−1.96
−37.01
14.25
CDD > 0°C April to June
820.27
4.17
−10.25
4.47
0.0229
dd
−42.89
−82.83
−2.95
Mn mo MEAN T April to June
8.98
0.05
−0.11
0.05
0.0235
°C
−0.47
−0.92
−0.03
Mn mo MAX T April to June
13.68
0.08
−0.59
0.09
<0.0001
°C
−2.47
−3.24
−1.70
Mn mo MIN T April to June
5.29
0.04
0.16
0.05
0.0006
°C
0.68
0.26
1.10
Significant year effects
Intercept 2012
PC1 2012
2012 Change in temperature metrics ~ PC1
SE
^
b1
SE
P
Units
Change
LCL
UCL
1.73
−7.84
1.85
<0.0001
dd
−32.82
−49.36
−16.28
1.62
0.03
−0.07
0.03
0.0341
°C
−0.27
−0.55
0.00
SD wkly T April to June
3.78
0.01
0.01
0.01
0.4512
°C
0.03
−0.04
0.13
MAX T warmest mo
25.22
0.14
−0.68
0.15
<0.0001
°C
−2.82
−4.15
−1.49
MIN T coldest mo
−1.14
0.06
0.33
0.06
<0.0001
°C
1.39
0.88
1.91
^
b0
115.06
SD wkly T January to March
CDD > 10°C April to June
Intercept 2013
PC1 2013
2013 Change in temperature metrics ~ PC1
SE
D^b 1
SE
P
Units
Change
LCL
UCL
0.71
2.72
0.71
0.0002
dd
−21.43
−37.97
−4.90
0.98
0.04
0.13
0.04
0.0014
°C
0.26
0.02
−0.53
SD wkly T April to June
1.08
0.01
−0.04
0.01
0.0071
°C
−0.11
−0.21
−0.02
MAX T warmest mo
1.17
0.06
0.19
0.06
0.0047
°C
−2.05
−3.38
−0.72
MIN T coldest mo
−0.61
0.08
−0.26
0.08
0.0013
°C
0.32
0.16
0.84
D^b 0
CDD > 10°C April to June
73.36
SD wkly T January to March
reported structural differences between old-growth forests and secondary
Douglas-fir (Pseudotsuga menzisii) forests in the Pacific Northwest (28).
After we statistically controlled for the effects of elevation, PC1 was
associated with most temperature variables (8 of 10 variables; Table 1).
Effects were largest for maximum and minimum temperatures, as well
as for CDDs in the spring and summer months. Temperature differences
were substantial across the gradient in forest structure; for instance, in
2012, maximum spring monthly temperatures decreased by 2.5°C (95%
confidence interval, 1.7° to 3.2°C; Fig. 4C) across the observed gradient
in forest structure (from structurally simple plantations to complex
old-growth forests). Minimum temperatures during this same period
were 0.7°C (95% confidence interval, 0.3° to 1.1°C; Fig. 4D) warmer
Frey et al. Sci. Adv. 2016; 2 : e1501392
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across the same gradient. Overall, these influences of old-growth
forests on thermal conditions were consistent between years (although
we found statistical evidence for year × PC1 interactions, parameter
estimates of the interactions tended to be small and only resulted in
a sign change for variability in temperature from January to March;
Table 1).
DISCUSSION
Elevation was a powerful predictor for air temperatures across years,
variables, and scales, confirming the importance of macrotopography
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No year effects
RESEARCH ARTICLE
Frey et al. Sci. Adv. 2016; 2 : e1501392
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inhabiting early successional forests (42), unless they are able to take
advantage of the microclimatic buffering of older forests or cooler microclimates that are near old-forest edges (34). Currently, early seral species are of high conservation concern in the Pacific Northwest, largely as
a result of habitat loss (42); given that early seral forests may not have
equivalent thermal refuges, we predict synergistic negative effects on
these species when combined with climate.
We conclude that the substantial influence of vegetation structure
on microclimate presents the opportunity to manage for conditions
that favor the persistence of biodiversity (43). By conserving or creating
forest conditions that buffer organisms from the impacts of regional
warming and/or slow the rate at which organisms must adapt to a
changing climate, it may be possible to ameliorate some of the severe
negative effects of regional warming. Given the time frame for forests
to acquire old-growth structural conditions, understanding thresholds
in forest structure where important ecosystem services are lost is of
pressing concern (44). With ~3.5 million ha of old-growth forests remaining in the coastal region of the Pacific Northwest (much of which
is now protected under the federal Northwest Forest Plan) (45), there
is substantial potential—in this region at least—to reduce the effects of
warming on native populations of forest species.
Understanding the fine-resolution effects of vegetation structure
on mediating microclimate could also improve predictions about biodiversity responses to climate change. Without considering the detailed
information on forest structural conditions reported in this paper, published model estimates of changes in species’ climatic niches (and extinction risk) (46, 47) could be substantially underestimated or
overestimated, depending on the amount of old forests in a landscape.
We demonstrate how combining new remote sensing technologies
(48) with machine-learning techniques provides an effective option
to develop high-resolution, spatially explicit models of under-canopy
temperature characteristics. As under-canopy temperature data become more readily available, microclimate variability can thus be
mapped across broad spatial scales. Under-canopy temperature
modeling, coupled with models predicting future vegetation dynamics
(49), offers the potential to enhance our understanding of microclimate
and species persistence in the face of climate change.
MATERIALS AND METHODS
Experimental design
We collected fine-scale temperature data using calibrated HOBO pendant data loggers (Supplementary Materials) at 183 locations across
the 6400-ha HJA in the Cascade Mountains of central Oregon, USA
(44°12′N, 122°15′W). Data loggers were placed 1.5 m above the forest
floor; this ground- and shrub-level forest stratum is characterized by
particularly high biodiversity in Douglas-fir forests of the Pacific Northwest (39–41). The HJA spans an elevational gradient from 410 to 1630 m
above sea level. It is a conifer forest mosaic that is largely composed of a
mix of old-growth forests, mature forests, and 40- to 60-year-old Douglasfir plantations. Sampling locations were stratified across elevation, forest
type, and distance to roads, ensuring that the full environmental gradient was sampled (Fig. 3) with a minimum distance between sampling
points of 300 m (Supplementary Materials). We collected air temperature data at the 183 sample locations from January 2012 to July 2013. In
total, we used 7,417,320 temperature loggings to calculate summary metrics (table S6).
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in temperature patterns (7, 20). However, although elevation predicted
most temperature metrics well, it was less effective in predicting temperature variability and degree-day accumulation from January to
March—both of which are microclimatic factors that are likely to
influence species behavior and demography (29). Microtopographic
variables, including slope, aspect, and relative topographic position,
also influenced temperature patterns, but this effect varied markedly
by time of year. Depressions and other topographically sheltered
areas are thought to contribute to the decoupling of surface temperatures from regional patterns, thereby potentially generating microrefugia in complex terrain (7). These topographic features exerted a
large influence on understory microclimate during winter, when
persistent cold air pools form in depressions and valleys.
Vegetation characteristics associated with older forest stands appeared to confer a strong, thermally insulating effect. Older forests with
tall canopies, high biomass, and vertical complexity (Fig. 1A) provided
cooler microclimates compared with simplified stands (Fig. 1B). This
resulted in differences as large as 2.5°C between plantation sites and
old-growth sites, a temperature range equivalent to predicted global
temperature increases over the next 50 years (30). This effect was potentially attributable to large differences in biomass between forest
types (16), rather than canopy cover, as we observed less variation in
canopy cover between old-growth sites and plantation sites (table S5).
Although previous studies have shown strong influences of vegetation on microclimate, most of these demonstrated differences between
significantly different vegetation types or stages, such as mature forests
versus grasslands (18, 31) or clearcuts (19, 32). At the global scale, forests
have been shown to have a broad-scale cooling effect (33); however, to
our knowledge, this is the first evidence that subtler structural differences within mature forest types (that is, mature plantations versus
old-growth forests) mediate under-canopy temperature regimes.
Our findings indicate that management practices that result in
single-species, even-aged plantations are likely to reduce the thermal
buffering capacity of forest sites, potentially limiting the availability
of favorable microclimates for some species. Unlike most predictors,
which were primarily useful when summarized at fine spatial scales
(25-m radius), vegetation at broader scales exerted the strongest influences on temperature. This is consistent with results from studies
that examine the thermal edge effects of high-contrast cover types (34);
smaller forest patches tend to be more susceptible to changes in temperature (20), and such edge effects also limit microclimatic buffering
of tropical forests (22). In jurisdictions where biodiversity maintenance
is the goal, conservation and restoration of structures associated with
old-growth forests are more likely to sustain favorable microclimates
(35) and to reduce climate change impacts on temperature-sensitive
species. Recent work shows that the understory microclimate differences documented here could be highly relevant to biodiversity conservation in temperate forests; cooler forest types have attenuated the
widespread loss of cool-adapted understory plant species (13) and have
promoted tree recruitment (36). Amphibians, lizards, insects, and even
large mammals are shown to take advantage of microclimate conditions when regional climate moves beyond the range of thermal preferences (5, 37, 38). However, our findings apply to species inhabiting
forest understory. Although a high proportion of forest biodiversity is
found in this stratum (39–41), species associated with upper canopies
may not benefit from the microclimate buffering capacity of oldgrowth forests. Furthermore, because older seral stages provide the highest levels of buffering, management options may be limited for species
RESEARCH ARTICLE
Environmental predictor variables
To model temperature metrics, we selected 19 predictor variables
(table S7) that we hypothesized to be important for influencing air
temperatures in forested mountain landscapes and categorized these
into three main groups: (i) elevation (ELV), (ii) microtopography
(TOPO), and (iii) vegetation structure (VEG). We derived all vegetation variables from LiDAR data collected at the HJA in August
2008 during the leaf-on period (50). LiDAR is a relatively new technology that allows for fine-scale mapping of forest structure across
broad spatial extents (51, 52). The VEG category variables described
vegetation structure using metrics relating to (i) canopy height, (ii) cover
at multiple strata, and (iii) vertical distribution of canopy elements.
Response variables
Our 10 response variables included CDDs, mean monthly minimum
and maximum temperatures, and variability in temperature (table S6).
CDDs are linked closely to timing of spring plant bud burst, leaf out,
and flowering, as well as insect emergence, and therefore have the
potential to influence higher trophic levels (53). Variability in weekly
temperature in both time periods (as measured by SD) may also determine the quality of sites (54). We chose the two time periods of
January to March (winter-spring transition) and April to June (springsummer transition) because they are relevant to the phenology of
many organisms on our landscape. In temperate regions, phenological events during spring also have direct implications for reproduction and growth in both plants and animals (55). We also
included the minimum and maximum temperatures of the coldest
and warmest months, respectively, as they represent extremes at
the annual time scale.
Statistical analysis: PCA and generalized linear
mixed models
We performed a PCA on all of our LiDAR-derived vegetation variables at the 25-m scale (13 variables) to test whether we could reliably
differentiate between plantations and older forests (table S3). This also
aided in determining whether our vegetation structure variables captured the gradient in forest structure present across the landscape. A
weakness of BRTs is that they do not produce effect sizes (that is, regression slopes) that can be easily related to differences in response
variables (degrees Celsius or degree days). Therefore, we used generalized linear mixed models [R package “nlme” (58)] to examine the
relationship between values of principal components (reflecting a
gradient in forest structure) and temperature metrics. We combined
data from both years and included “site” as a random effect to account
for a lack of independence within location between years. Elevation
was included as a covariate in all models. We tested for a year effect
on the influence of PC1 on microclimate via an interaction model. In
this fashion, we were able to quantify differences in temperature across
the old-growth forest structure gradient and to test for consistency
between years.
SUPPLEMENTARY MATERIALS
Statistical analysis: BRTs
We used a machine-learning approach (BRTs) to explore the relationship between our suite of predictor variables (19 × 2 spatial scales = 38
total predictor variables; table S7) and air temperature at our 183 sample locations. BRTs have recently been used extensively in species distribution modeling because of their capacity for uncovering nonlinear
relationships between predictors and response variables, as well as
their flexibility in testing interactions among predictors (25). BRTs
can also handle large numbers of predictor variables and the colinearity between them (25), which is advantageous in studies such as ours,
where there are many categorized predictor variables (table S7) but
little prior information about which are most important or at which
spatial scales. This modeling method afforded the flexibility to explore
multiple potential correlates of microclimate without arbitrarily restricting our predictor set. We used the R program version 3.0.1 (56)
in combination with the “dismo” package version 0.8-17 (57) for all
analyses (Supplementary Materials). We used predictive deviance,
measured as the mean deviance from the held-out data in all folds,
as our primary measure of model performance. We also tested the
Frey et al. Sci. Adv. 2016; 2 : e1501392
22 April 2016
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/
content/full/2/4/e1501392/DC1
Supplementary Materials and Methods
fig. S1. Fine-resolution (5 m) spatial predictions of temperature metrics at the HJA based on
BRT models.
fig. S2. Partial dependence plots showing the relationship between selected microtopographic
variables and microclimate.
fig. S3. Partial dependence plots showing the relationship between selected vegetation
structure variables and microclimate.
fig. S4. Key interactions identified from BRT models testing the effects of elevation, microtopography,
and vegetation structure on microclimate.
fig. S5. RI of variables measured at 25- and 250-m scales for each temperature metric in both years.
fig. S6. Comparison of observed microclimate data by year.
fig. S7. Comparison of predicted microclimate metrics by year.
fig. S8. Photo of the HOBO temperature sensor in the field.
table S1. BRT model settings (learning rate, number of trees), performance diagnostics
(deviance, deviance SE, CV corr, CV SE), and tests for spatial autocorrelation in the BRT model
residuals (Moran’s I and P).
table S2. Pearson’s correlation coefficients (r) and associated P values for both observed and
predicted values between years.
table S3. Results from a PCA of all vegetation structure predictor variables.
table S4. Summary statistics and t tests showing differences in LiDAR metrics between mature
plantations and mature/old-growth forests.
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Downloaded from http://advances.sciencemag.org/ on May 2, 2017
Spatial scales
We assessed the importance of our predictor variables at two spatial
extents around each sample point because the scale at which drivers
of microclimate act is largely unknown (29): (i) at a radius of 25 m,
which represents local-scale predictors, and (ii) at a radius of 250 m,
which represents stand-scale conditions. The relative roles of the biotic
and abiotic aspects of the environment could influence microclimate
differently at these two spatial scales (3).
correlation between predicted and observed temperature metrics
via a built-in 10-fold cross-validation that uses 10% independent “test”
data. These tests thus represent an entirely independent test of model
performance. If overfitting occurred, these tests should show low correlations between predicted and observed values. We also tested for
spatial autocorrelation in our temperature data set (Supplementary
Materials).
We assessed the contribution of each group of predictor variables
(ELV, TOPO, and VEG) to the explained variance in temperature
metrics by summing the RI values of the variables in each category (Supplementary Materials). To determine the direction and nature of the
relationships between the temperature metrics and the most influential
individual predictor variables (>2% RI), we examined the partial dependence plots for the visualization of the fitted functions.
RESEARCH ARTICLE
table S5. Results from Welch two-sample t tests comparing measures of biomass and canopy
cover for plantation sites and mature/old-growth forest sites.
table S6. Temperature metrics used in our study and associated summary statistics.
table S7. Predictor variables used to predict patterns in microclimate metrics.
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Acknowledgments: We extend our special thanks to T. Valentine for her help with GIS (geographic information system), to C. Murphy and E. Miles for assistance with the temperature data
processing, and to J. Sexton for providing logistical support for field work. We also thank C. Still,
whose comments greatly improved this manuscript. This work would not have been possible
without our field assistants (E. Jackson, A. Bartelt, S. Ashe, S. Yegorova, A. Mott, and K. Stanley).
Funding: This research was made possible with support from multiple grants and awards: an
NSF–Integrative Graduate Education and Research Traineeship fellowship (NSF-0333257), a Department of the Interior Northwest Climate Science Center graduate fellowship, and an Andrews
Forest Long-Term Ecological Research graduate research assistantship (NSF DEB-0823380) all
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manuscript. Competing interests: The authors declare that they have no competing interests. Data
and materials availability: All data needed to evaluate the conclusions in the paper are present in
the paper and/or the Supplementary Materials. Air temperature and LiDAR data are available online
at the Andrews Forest Webpage (air temperature: http://andrewsforest.oregonstate.edu/data/
abstract.cfm?dbcode=MS045; LiDAR: http://andrewsforest.oregonstate.edu/data/abstract.cfm?
dbcode=GI010). Additional data related to this paper may be requested from the authors.
Submitted 6 October 2015
Accepted 25 March 2016
Published 22 April 2016
10.1126/sciadv.1501392
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Spatial models reveal the microclimatic buffering capacity of
old-growth forests
Sarah J. K. Frey, Adam S. Hadley, Sherri L. Johnson, Mark
Schulze, Julia A. Jones and Matthew G. Betts (April 22, 2016)
Sci Adv 2016, 2:.
doi: 10.1126/sciadv.1501392
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